{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da86632e-85cd-45e3-ab9b-fa5f0c932968",
   "metadata": {},
   "source": [
    "# 图都很普通，参数全默认。想好看自己画，其它的代码逻辑可以参考。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:38.570779900Z",
     "start_time": "2023-11-12T01:32:38.116920400Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\r\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\r\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\r\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\r\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4c218cdee0a22c",
   "metadata": {},
   "source": [
    "# Task2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a117052e2a92dac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:39.946663Z",
     "start_time": "2023-11-12T01:32:39.898096700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stkcd', 'Accper', 'Typrep', 'B001100000', 'B001101000', 'B001200000',\n",
       "       'B001201000', 'B001207000', 'B001209000', 'B001210000', 'B001211000',\n",
       "       'B001212000', 'B001301000', 'B001302000', 'B001302101', 'B001304000',\n",
       "       'B001300000', 'B001400000', 'B001500000', 'B001000000', 'B002100000',\n",
       "       'B002000000', 'B002000101', 'B002000201', 'B003000000', 'B004000000',\n",
       "       'B005000000', 'B006000000', 'B006000101', 'B006000102', 'A002000000',\n",
       "       'A001000000', 'Indnme', 'Nindnme', '利润率', '资产负债率'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../result/LR_new.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84381c3ea107e550",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:40.419165900Z",
     "start_time": "2023-11-12T01:32:40.383166200Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stkcd</th>\n",
       "      <th>Accper</th>\n",
       "      <th>Typrep</th>\n",
       "      <th>B001100000</th>\n",
       "      <th>B001101000</th>\n",
       "      <th>B001200000</th>\n",
       "      <th>B001201000</th>\n",
       "      <th>B001207000</th>\n",
       "      <th>B001209000</th>\n",
       "      <th>B001210000</th>\n",
       "      <th>...</th>\n",
       "      <th>B005000000</th>\n",
       "      <th>B006000000</th>\n",
       "      <th>B006000101</th>\n",
       "      <th>B006000102</th>\n",
       "      <th>A002000000</th>\n",
       "      <th>A001000000</th>\n",
       "      <th>Indnme</th>\n",
       "      <th>Nindnme</th>\n",
       "      <th>利润率</th>\n",
       "      <th>资产负债率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600705</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>A</td>\n",
       "      <td>2.779306e+09</td>\n",
       "      <td>1.470703e+09</td>\n",
       "      <td>1.692376e+09</td>\n",
       "      <td>8.251063e+08</td>\n",
       "      <td>15429945.69</td>\n",
       "      <td>3.448499e+08</td>\n",
       "      <td>2.534629e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>54279195.87</td>\n",
       "      <td>1.092024e+09</td>\n",
       "      <td>8.471375e+08</td>\n",
       "      <td>2.448865e+08</td>\n",
       "      <td>1.985080e+11</td>\n",
       "      <td>2.276480e+11</td>\n",
       "      <td>����</td>\n",
       "      <td>��������ҵ</td>\n",
       "      <td>48.831026</td>\n",
       "      <td>87.199536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>921</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>A</td>\n",
       "      <td>8.974222e+09</td>\n",
       "      <td>8.974222e+09</td>\n",
       "      <td>8.824302e+09</td>\n",
       "      <td>7.368462e+09</td>\n",
       "      <td>57149348.38</td>\n",
       "      <td>1.126095e+09</td>\n",
       "      <td>2.629585e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>-4026373.80</td>\n",
       "      <td>3.020331e+08</td>\n",
       "      <td>2.839604e+08</td>\n",
       "      <td>1.807269e+07</td>\n",
       "      <td>1.503353e+10</td>\n",
       "      <td>2.228894e+10</td>\n",
       "      <td>��ҵ</td>\n",
       "      <td>������е����������ҵ</td>\n",
       "      <td>4.011893</td>\n",
       "      <td>67.448411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2286</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>A</td>\n",
       "      <td>3.868472e+08</td>\n",
       "      <td>3.868472e+08</td>\n",
       "      <td>3.780943e+08</td>\n",
       "      <td>3.326991e+08</td>\n",
       "      <td>3758810.87</td>\n",
       "      <td>2.362274e+07</td>\n",
       "      <td>1.179122e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>-3218783.24</td>\n",
       "      <td>1.095736e+07</td>\n",
       "      <td>1.105461e+07</td>\n",
       "      <td>-9.724987e+04</td>\n",
       "      <td>7.540687e+08</td>\n",
       "      <td>2.290365e+09</td>\n",
       "      <td>��ҵ</td>\n",
       "      <td>ʳƷ�ӹ�ҵ</td>\n",
       "      <td>4.327788</td>\n",
       "      <td>32.923518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>546</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>A</td>\n",
       "      <td>1.328454e+09</td>\n",
       "      <td>1.328454e+09</td>\n",
       "      <td>1.341649e+09</td>\n",
       "      <td>1.241990e+09</td>\n",
       "      <td>9290554.13</td>\n",
       "      <td>1.779985e+07</td>\n",
       "      <td>4.566993e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>-360.17</td>\n",
       "      <td>1.865989e+07</td>\n",
       "      <td>5.779797e+06</td>\n",
       "      <td>1.288009e+07</td>\n",
       "      <td>4.021656e+09</td>\n",
       "      <td>7.916889e+09</td>\n",
       "      <td>������ҵ</td>\n",
       "      <td>����������ҵ</td>\n",
       "      <td>1.930558</td>\n",
       "      <td>50.798441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600710</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>A</td>\n",
       "      <td>1.732878e+10</td>\n",
       "      <td>1.732878e+10</td>\n",
       "      <td>1.717469e+10</td>\n",
       "      <td>1.644967e+10</td>\n",
       "      <td>10379980.72</td>\n",
       "      <td>2.684930e+08</td>\n",
       "      <td>2.505174e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>-27348794.86</td>\n",
       "      <td>1.837843e+08</td>\n",
       "      <td>6.712745e+07</td>\n",
       "      <td>1.166569e+08</td>\n",
       "      <td>3.636285e+10</td>\n",
       "      <td>4.360007e+10</td>\n",
       "      <td>��ҵ</td>\n",
       "      <td>����������ó��</td>\n",
       "      <td>1.617851</td>\n",
       "      <td>83.400901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Stkcd      Accper Typrep    B001100000    B001101000    B001200000  \\\n",
       "0  600705  2018-03-31      A  2.779306e+09  1.470703e+09  1.692376e+09   \n",
       "1     921  2018-03-31      A  8.974222e+09  8.974222e+09  8.824302e+09   \n",
       "2    2286  2018-03-31      A  3.868472e+08  3.868472e+08  3.780943e+08   \n",
       "3     546  2018-03-31      A  1.328454e+09  1.328454e+09  1.341649e+09   \n",
       "4  600710  2018-03-31      A  1.732878e+10  1.732878e+10  1.717469e+10   \n",
       "\n",
       "     B001201000   B001207000    B001209000    B001210000  ...   B005000000  \\\n",
       "0  8.251063e+08  15429945.69  3.448499e+08  2.534629e+08  ...  54279195.87   \n",
       "1  7.368462e+09  57149348.38  1.126095e+09  2.629585e+08  ...  -4026373.80   \n",
       "2  3.326991e+08   3758810.87  2.362274e+07  1.179122e+07  ...  -3218783.24   \n",
       "3  1.241990e+09   9290554.13  1.779985e+07  4.566993e+07  ...      -360.17   \n",
       "4  1.644967e+10  10379980.72  2.684930e+08  2.505174e+08  ... -27348794.86   \n",
       "\n",
       "     B006000000    B006000101    B006000102    A002000000    A001000000  \\\n",
       "0  1.092024e+09  8.471375e+08  2.448865e+08  1.985080e+11  2.276480e+11   \n",
       "1  3.020331e+08  2.839604e+08  1.807269e+07  1.503353e+10  2.228894e+10   \n",
       "2  1.095736e+07  1.105461e+07 -9.724987e+04  7.540687e+08  2.290365e+09   \n",
       "3  1.865989e+07  5.779797e+06  1.288009e+07  4.021656e+09  7.916889e+09   \n",
       "4  1.837843e+08  6.712745e+07  1.166569e+08  3.636285e+10  4.360007e+10   \n",
       "\n",
       "    Indnme             Nindnme        利润率      资产负债率  \n",
       "0     ����           ��������ҵ  48.831026  87.199536  \n",
       "1      ��ҵ  ������е����������ҵ   4.011893  67.448411  \n",
       "2      ��ҵ              ʳƷ�ӹ�ҵ   4.327788  32.923518  \n",
       "3  ������ҵ         ����������ҵ   1.930558  50.798441  \n",
       "4      ��ҵ       ����������ó��   1.617851  83.400901  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a44d49b6d51650f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:40.650357400Z",
     "start_time": "2023-11-12T01:32:40.635356500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 将Accper列转换为日期格式  \n",
    "df['Accper'] = pd.to_datetime(df['Accper'])\n",
    "\n",
    "# 定义日期范围，筛选2018年9月份的数据  \n",
    "mask = (df['Accper'] > '2018-08-31') & (df['Accper'] <= '2018-09-30')\n",
    "df_sep = df.loc[mask]\n",
    "\n",
    "# 再筛选出合并报表的数据  \n",
    "df_sep = df_sep[df_sep['Typrep'] == 'A']\n",
    "\n",
    "# 计算各行业大类的利润总和均值  \n",
    "mean_profits = df_sep.groupby('Indnme')['B001000000'].mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0fe372a2e83575d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:41.558205500Z",
     "start_time": "2023-11-12T01:32:40.835906400Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 绘制柱状图  \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(mean_profits.index, mean_profits.values)\n",
    "plt.title('2018 年9月各行业大类的利润对比')\n",
    "plt.xlabel('行业大类')\n",
    "plt.ylabel('利润总额均值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a7f76b9921081cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:41.710196800Z",
     "start_time": "2023-11-12T01:32:41.555203900Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAHtCAYAAAAA11hTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABspElEQVR4nO3deXxU1f3/8dfJZLKQQBYSSEiCLCqightVcRcQsIhFrdWqv9aqtVrbr9rWFttqqd1sbau0tS7VtrZaW9ywlCouuONS3JVFZJFMSCSQhSRkmSTn98edQBIm+8zcWd7PxyMPkjt37v3kMuJ7znzuOcZai4iIiIiIdJXkdgEiIiIiItFIQVlEREREJAgFZRERERGRIBSURURERESCUFAWEREREQlCQVlERGKeMcb09bgxxhOpekQkPigoi0hYGGO+YYy5zO06opUx5jxjzPUunfssY4y3H/vNNsb8JPC9p3PQDATP1BDWNNYYs38f+xQaY74V+P4zxpinA98fBizrtm+mMWaqMebLxpi/AduAGzs9PsoYU9bp53HGmILA90cZY74zwPovM8Z8bwD7n2aMGdeP/fr8exKR8FFQFolTxpiFxphaY0yNMebbnbZfYYypMMasNsaM7/acHxhjFnXbdqMxxmeMKTPGXN7Pc5cAPwc2B3ksuXvAMsZcaIz5S5B95xtjPjXGvBzka1e3fc8zxrwX+L1WG2Pe6fT9amPMu8aYBzrt/yVjTKUxZrcx5jedti8wxpQaY9YaY47src5AWPypMWajMWaDMebsfl6f4cCvgU+DPOYxxqT15/gDuT6dnnMUsAjodRJ9Y8wI4DbgP4FN84E91xN4k27htNNzPzbGnG+MOcIYs6O383RyNvDTPvZpAq4wxpwP+IFmY0wSsLhTnR2+Fth+HlAAnAT8uNPjLYFjdJgCvGiMmQCsAS4NhPdlxpi3A7/3B8aYiwK/40+NMS8ZY543xjwPXAss7PjZGPOiMea1wL7GGJNiTJdR75OAb3X8ENjHa/Yd9f6XMeaCwD4/6/zfsohEgLVWX/rSV5x9AdOBd4EJwMHALqAEJwyUAWMC+yzv9JyvAruBRZ22HQo8C3gCz98OjO7j3MnAc8AG4PnAVz3wWuD7V4B7O+1/BlAD/DXIseb2sD0Z2NZLDYcDG4H0Hh4fC2wK7FcE+IDjgVHADuCQwLVb3VudwPnASiANGI8zajm+H38/fwdKO12fHcA7ge9fBv7bn+MP5voAT+IEztWdvlqB+d2e/yTwrUG+/v4XuF4TgU39fM6VwG96eTwl8Do8Brg98Hf3H+BA4FHABPZJ6va8Bd3+zo4NvAazgS3d9j0MyAV+AmQGqWERcFng+1HAhMD3I4E3As8dFtg2Bjg28P1o4APgrcD1rgbWB/7OO/4u3g7sM7vT+Q4GPgFSO53/msD3qcA1gCdc/47oS1/6siQjIvGoAfiitXYTgDFmC87/uGcDf7PWbgO2GWPyjDEZgefMBX7f7TiTccJiG1AaOE4BQUZCA+cxwF04wWCatbY2sP0d4Hxr7ZYgT/sKTjCZEuSxVuB0Y8zLQR5r6aEGD85I6EJrbaMxZgYww1r7w067JeNcn3cCz3kLKMYJx09baz8MbN9qjDnQWvtRD3XOwwn9TcBmY8ybOEFsn5H0TvX9CDgNONpauzWwbSlwm7X2+W6793X8AV0fY8z/AXXAw8C/rLXLjDG3AC9ZazuPDufghLe/Bq7NWdbaT3r6nQLH/gFwj7X2U5zXXyvQBrQHHt8f+A5wlbW2LTBi3Wit7RjVTQYaOx0vBRgGNAT2uROYihMsU4Cv4wTOBwPbXsIJj18G1gRG/r04f68lgU8TUoD76DqSjDHmIOAynNdMqzHmSuB5Y8xvA/sa4EWgttPTpgK3G2M+D3wXuAXnzdZCY8y5wGPAEuC1wDU51Bjzw8C1vxnnNfoBzhvIs4BjrLUPd7ustwL/ttY2B7nkfwSygD8EeUxEQkRBWSQOWWvf6/jeGDMGZzT4A+ASnFHLDtuA/ay1a4BzTLe2C5yPoG8yxtwG7A/kA2t7OXUWUInzEfqTxpiO/8Hvj/MRcitO+LnSWvta4LHP44Sbnjxhrb248wZjTDLwcfcdA6H/HpwRxIcCH8t/ACw2xlRYa/8AEHgD0fEmYhhO+LwaZwT3vU6H/CRQ+0e91JkXOI4H541FeU+/iHFaTgxwFfCQMaYjGB4KjDfG1AHpwM+ttY/08/j9uj7GmHyc9olzcNrunjTGfBEn1H6t877W2krgh8aYe3ACYp0xpgLnk4l2nMDZ0brxqrX2IpyQ2AT8huDmAvsH3nSBM2K9nzGm4zjDgSRjTMfvYnCC7xk4YfOSwO8xBvgr8DucEeuHgCuAazu9psBpbWkHfhio66eButOD1FYGHA08bYyZF9j/eeAzwL9xRru3AXt6yq21zxhjzsN5A3oMzqcUyTihdxjwM2vtvzv2N8acifNm644g508HbjXGZFlr7w3sf03g/N3/e/MYY+4FRuC82WsNcjwRCREFZZH49zPgLmttQyBsde5dbcD5CLona3BaBJbjBJlfW2uDjuQCWGtrcEbU5gLrOwJcYET5vGAjytZaa3qesGCg/0b9EyeQHR8I/cOB93FC8feNMRustSu6Pec6YKW1dnPg+lR3emzP9emhzgeBvxtjMoETcQJSsNFdAsdoBhYFRjAbrbWnQK8jyn0dv9/XJxB+TzPG5OC8IWjHaRVIBT5njHnRWruzY/9AL+6lwBHW2iqcTxI6HvspsOeNR8BfgBvoOSifDvy5Uz3HdX4wEP4mWGtPDfZkY8zBOG/0jge+hzNi/hNr7X2B19dfjTEvWGuvCRz/Q2PMaJygnQ4cb629xxhzeJBrUxd4zV5trd0d+HtuZ+99PJ7Az53rSQ38TutxWiC+BPwfsBSnX/l6Y0y1tfYlY0w6zqcRZ1prdwbeyHhwgrvXWvuRMWY2zpunx4AMnPB/Pc6bo85+CPwNuLzTmw4RCRMFZZE4Zow5HTgBp58TnBCY3WmXdLoFgG4uBjZba2cHRmdXGGPesNb+r49T93bMgcgGzjDOzWP9cQHOR/XtxphZwBXW2s8DGGOK6fomoWO2hCuAowKbBnR9rLX/DXz0Phdn9O/n/Rzh69f16cfxs+nH9QmMwl6CE7Y9wL+A0wJvng4BLgS+Z4wpwmkLyQRuwmkL6K9lwL2Ba9r9/ONxRl3P6aG+DGAWTjvQMdba14PslhKo57uBv98SnECJtfZdY8x09g2Vi3H6kWuBrxpj6nGC7T4CAfnmTpsKgcdxPlF4BGeEurPHgVVAFc6nGGU4o8/v4bz5uhjnTeOrgRagY4A3jDF+4MjAVy0wNvD39xAwxVprgSpjzFTgzE7X6Eyc1+q91toBzcghIoOnoCwSpwJB4h6c/tKGwObVOGHpgUA/8ZE4N7L15FicUWUC4eQdYBrOzVrBznkZTu9oMjDCOLMBgBM2/mmMacL5SD0TZwT17338GoU4N3j9ott5grZe4ISZ6caYFpzR5PxACEkGhltrJ3Y6RhZOYPx6oGcbnOtzQ6fjTcPpNe2RtfY5Y8xO4CKCf6zeue7TcWYDaQOKOl2fQ4FxxpiawM8ZwMPW2l/2cfz+Xp+dOH3Nd+K0EOQBV3UaIS8AfgTc3+mThxkMoP/VWttkjLmTTiPPnRzB3l7rYH6EEzwfAO4yxhwd5JOLX+G8MbgmUHchzjV7nb19xBnGmFsDo8zX4rRD/BoncJ6L8zrIp+f/9600gRkmAq+JzxhjnsR5w7WlozUp8KbxAqA6EGz3D7z2D7LWfscYkxa4Hl8N1NVxfT6D82bhTOCCwH9TCwK//x8Dx+q4ni0dfz/GmEdwbhh8gt7/exWREFNQFolDgY+F/w3cYq19o9NDTwC/NcasxAlnO621vf2PdzNwXiDQ5QBfDHwFZa29Byecd6/nHXq+ma83p+EEy36x1u7pte0+ohzEg8CT1trOQfgN4IBA6EnCCfhvBHtyNwuBn1prd/dR3xM4fwdd9NJ60dfx+3V9Ai0fDwTOVWWtPbzb+X8KtHS8oQp8pL+ll5aYns7z3cDxru+2/VGcmSn2EegJ/hzOzY21xpingD8bY77cubXAWju703PScEaX/4jTq3tJ532NMYU4bRCn4IR07N4bJ7fhtJ50r+NkwG+tLTfGYJz5mj+P03f/T2PMpzgzU4DzBmox0NKpx7oQGGaMmRY4Hjivocdwbsrr+Hv4h3Hma14a+O/wQmCutbYu6EV1fBunX/5H3WqeBJRba4NOBSgiQ6egLBKf5uK0WxQaYxYGtl1lrX0k8FH+LUAzzv+ke3M7TlvCizgjY4uttS8Nop4kBjhvu3Hm+52A89F55+3JOKOWbd22pwHNnUflghyzY47aQ3H6Sz9jnDl5AW621t5mjPkczo1iI4BzeuvJDhzzUJxr1NsNiX3p8fr0dPyBXp9Oetoe7LolERgR7XT8DJzwWN5pWwqBWSv6uP4pONOZNQb+vhbi3Eg4xwZmSMHpy30c52bDy621m7sd42ScPug/WmvvNs7CIK8YZzaRp6yj3BgzOTCK22Uu7MC2XTj/fdjAMQ1OL/8tHb+3tfa3wG+7nfsXziHsGzjTK3Z+bM+Ick+/f2C/8TizexyCE+L/C0w1xqzDCb2dW2uSANPxBtMY044znWGHRThvZr/f2zlFZPAUlEXikLX2cboFnE6PvYrTtxzssUXdfq7D+ch6qFJxekyDstb+FWcmA2BPoL0buC5I8PoLzgjk3d22r8ZZgKLL/t36d5OBG6wzFVpP12cdzjR6fdYZ2PYBMCnY/gPQ4/UJdvxBXp8OOYER/s4K6Npy0iElSF33AQfQ9TrMwmnraOx0/W8L/NkaCIHg/J53B0bQVwIf4kyLtrXjQNaZOu4cnFHYD4wxh1lrPzbONHZn48xi8jVr7ZuB/X9tjHkBZ8q1vxpjZltr3+/U5uEN8jtMBe5n782FJTjBv2PRkuHdL4QxZjFO29L9+1ylvecJuoqecVbXW4YTjtfjfNpzDE7P/Dk4vccnAsnGmIOttRU91P40cKcxZn7g5yqcm/tEJExML2/+RURcY4xJtUHmjzXGJFlrQ3WzYMwa7PUxxoyy1m4Pb3V9M8acZK19sY99Dg28UeiYh3mHdWZW6Wn/cYNo74kI49woud3unTc62D7D+2jBEJEIU1AWEREREQliQD2DIiIiIiKJQkFZRERERCSIqLyZLy8vz44bN87tMkREREQkzr355ps7rLX5wR6LyqA8btw4Vq/u70JcIiIiIiKDY4z5pKfH1HohIiIiIhKEgrKIiIiISBAKyiIiIiIiQURlj7KIiIhILPH7/fh8PpqamvreWVyRlpZGcXExXm/QRTSDUlAWERERGSKfz8fw4cMZN24cxhi3y5FurLXs3LkTn8/H+PHj+/08tV6IiIiIDFFTUxMjR45USI5SxhhGjhw54BF/BWURERGREFBIjm6D+ftRUBYRERERCUI9yiIiIiJxYNasWbS2tgZ9rLi4mPvvv79f+/T3WIlAQVlEREQkwpa+XcYtK9azraaRMdnpXDdnEguOKBrSMRcuXMisWbOCn2/p0n7vM5D94p2CsoiIiEgELX27jOsffZ9GfxsAZTWNXP/o+wBDDssSWgrKIiIiIiH042Ufsmbbrh4ff3trDS1t7V22Nfrb+O7D7/HgG1uDPufgMSP40fxDQlqn9E0384mIiIhEUPeQ3Nd2cY9GlEVERERCqK+R3+NvXklZTeM+24uy0/nX16aHqywZBI0oi4iIiETQdXMmke71dNmW7vVw3ZxJLlUkPVFQFhERSWC1y5axYcZM1k4+mA0zZlK7bJnbJcW9BUcU8Yuzp1CUnY7BGUn+xdlTdCNfFDLWWrdr2Me0adPs6tWr3S5DREQkrtUuW0b5DTdiOy3ra9LSKPzJTWTNn+9iZbFn7dq1TJ482dUazj33XCorK4M+dvjhh3Pbbbf1a5/+HisWBft7Msa8aa2dFmx/BWUREZEEtWHGTFq3bdtne/KYMRyw8lkXKopd0RCUpW8DDcpqvRAREUlQreXlA9oukmgUlEVERBJUcmFh0O0mPZ3WHj52F0kkCsoiIiIJatS114Cn6+wLeDzYxkY+nj2H7YsX01Zf70ptItFAQVlERCRBZc2fT+rkyU5YNobkMWMYc/MvmPjEf8k85WR23nEnG2edxs6//pX2lha3yxWJOC04IiIiksBMezsZxx3H2D/d3WV78a230njpZVT+9jdsv/mXVP3tb+R/8//IOnM+pvsotAzce0vg2Zug1gdZxTDzRpj6hSEdctasWbS2tgZ9rLi4mPvvv79f+/T3WIlAQVlERCSBtZSVMeKwqUEfSz/0EMb++c80rFrF9t/8lvLrr6fqz38m/1vXknnKKRhjIlxtnHhvCSz7P/AHVuerLXV+hiGF5YULFzJr1qygjy1durTf+wxkv3inoCwiIpKg2nbtor22lpTi4l73yzjuOMYdeyx1K1aw/bbb8F35ddKPOopR3/42w448IkLVxpAnFkLF+z0/7vsftDV33eZvhMe/AW/eF/w5BVPg9JtDV6P0i4KyiIhIgvL7fAB4i0v63NckJTHi9NMZPmsWNY88QuXtt/PJBReQOWMG6UccTvWD/6S1vJzkwkJGXXuNFizpTfeQ3Nd2cY2CsoiISIJq2ROU+790svF6yTn/fLLOPJOqv/2NyjvupH7lyj2Pt27bRvkNNwIkbljua+T31kOddovuskrgK8vDU5MMima9EBERSVB+XxlAn60XwSQNG0beFVeQnJO9z2O2qYntt942xOri2MwbwZvedZs33dkuUUVBWUREJEH5faUkjRiBJytr0Mdo/XR78O1a3a9nU78A83/njCBjnD/n/27Is15I6Kn1QkREJEG1+HwDarsIJrmwkNZt2/bdXlAwpOPGvalfUDCOAQrKIiIiCcrvKyN14sQhHWPUtddQfsON2KamLtuTCwuw1moKuQi66667+OlPfxr0scMPP5wFCxb0a5/+HisRKCiLiIgkINvejr+sjMyTTx7ScTpu2Nt+6217Zr1IO/QQ6p96mqq/3sfIr1wcgmqlPx566KGQ7DOQ/eJdWIOyMWY08KS19ghjzL3AZOC/1trgb1FEREQkIlord2Cbm/GWDPxGvu6y5s/vMsOFbW+n7Oqr2X7LLaQeeACZxx8/5HOIuCHcN/P9Gkg3xpwNeKy1xwFjjDEHhPm8IiIi0gt/mTM13GBmvOiLSUqi8Bc3kzpxAmXf+jYtW7eG/BwikRC2oGyMmQE0ABXAKcCSwEMrgROC7H+5MWa1MWZ1ZWVluMoSEREROi82EvqgDODJzKD49tsB8F31DdobGsJyHpFwCktQNsakADcCCwObMoCywPe7gNHdn2OtvdtaO81aOy0/Pz8cZYmIiEjAnsVGioY260VvUsaOpei3v6F540a2Xf99rLVhO5dIOISrR3khcLu1tiZwt2s90DGzdiaav1lERMRV/lIfyaNGkZSaGtbzZB5/PKO+8x22/+pX7LzzTvKuvDKs54sVyzctZ/Fbi6loqKAgo4Crj7yaeRPmDemYs2bNorW1NehjxcXF3H///f3ap7/HCsd+0SZcQXkWMMMYcxVwODAWKAVeAw4D1ofpvCIiItIPfp8vbG0X3eV+5WKa1q2lcvHvSJ10EMNnnBqR80ar5ZuWs2jVIpranCn1yhvKWbRqEcCQwvLChQuZNWtW0MeWLl3a733c3C/ahCUoW2tP6vjeGPM8cCbwkjFmDHA6cGw4zisiIiL901LmY9i0aRE5lzGGwptuomXjJrZddx3jlvxryPM3R7NfvvFL1lWt6/Hx9yrfo6W9pcu2prYmbnzlRh7+6OGgzzko9yC+d/T3Qlqn9C3sLRDW2lOstbtwbuh7DTjVWlsb7vOKiIhIcLalhdaKT8My40VPktLSKP7D7zFpafi+fhVtu3ZF7NzRpntI7mu7uCdiC45Ya6vZO/OFiIiIuMRfXg7t7XiLSyJ6Xm9hIcWLb+OTi79C2Xe+Q8kdd2A8nojWEAl9jfzOfng25Q3l+2wvzCjkL3P/Eq6yZBB0U52IiEiC2TPjRXH4ZrzoybBp0yj44Q9oePElKm9bHPHzR4Orj7yaNE9al21pnjSuPvJqlyqSnmgJaxERkQTj9zkztkay9aKznPPPp2nNWnb+6U+kTT6IEZ/9rCt1uKXjhr1Qz3ohoaegLCIikmD8Ph94vSSP3mdZg4gp+OEPaP74Y7Z9/wekjB9P2uTJrtXihnkT5ikYxwATjZN/T5s2za5evdrtMkREROKS79praVqzhv1XrHC1jtbKSjZ//lyMx8O4hx8iOTfX1XqGYu3atUx2Oeyfe+659LS68eGHH85tt93Wr336e6xw7Bduwf6ejDFvWmuDTgGjoCwiInGtdtkytt96G63l5SQXFjLq2mvImj/f7bJctfncL+AZPpyxf77X7VJofP99PrnwItKPOIKx9/wJ4/W6XdKgRENQlr4NNCjrZj4REYlbtcuWUX7DjbRu2wbW0rptG+U33EjtsmVul+aqSC420pf0KVMouOnH7H79dT791S1ulyPShYKyiIjEre233oZtauqyzTY1sf3W29wpKAq01TfQVl0dNUEZIHvBAnK//CWq//531h87nbWTD2bDjJkJ/4ZG3Keb+UREJG61lu87V21v2xOBv8yZGi6lJHqCMkDq5IMhKYn2mhqAPaP/QMK3yoh7FJRFRCRuJRcWOm0XQbYnKv+eOZSjKyhX/u530N7eZVvH6L+Ccv/MmjWL1tbWoI8VFxdz//3392uf/h5rIPvFKgVlERGJW6OuvYbyG27s0n5h0tIYde017hXlsmgNyok2+h+Om0wXLlzIrFmzgj62dOnSfu8Tjv1ilYKyiIjErY7g8enPf0FbdTWe/HxGf/e6hB6hbPGVkTRsGJ7sbLdL6SKRRv87bjLteAOnNpPopaAsIiJxLWv+fFL2248tXziPwh8vYviMGW6X5Cp/aSnekhKMMW6X0kU8jf5X/PznNK9d1+Pjje++i21p6bLNNjVR/oMfUrPkoaDPSZ18EAXf/35I65S+KSiLiEjc85aUAE5ITHT+Mh/esfu5XcY+OkZSE2HO6+4hua/t4h4FZRERiXue7GyShg2jxVfmdimustbS4isj47jj3C4lqKz58+MiGPc18rthxszgbSZjxrDf3/8WrrJkEDSPsoiIxD1jDN6SkoQfUW6rqsI2NuItiq4b+RLNqGuvwaSlddkWq20m8U4jyiIikhC8xcX4t37idhmu6nij4I2yOZQTTSK1mcQ6BWUREUkIKcVFNKxahbU26m5ki5SO1pOUKJsaLhGFo83krrvu4qc//WnQxw4//HAWLFjQr336e6yB7BerFJRFRCQheItLsI2NtO3cSXJentvluGLPHMpFRS5XIuHw0EPBZ8wY6D7h2C9WqUdZREQSgrfYCYcdYTER+ct8eEaOJGnYMLdLEYkJCsoiIpIQUgJTxLWUJm5Qbin1qe1CZAAUlEVEJCF0tBv4yxI3KPt9vqhbulokmikoi4hIQkhKT8eTl0dLgrZe2NZW/OXlCsoiA6Cb+UREJGGkFBfjT9DWC39FBbS17enVlvgza9YsWltbgz5WXFzM/fff3699+nusWNhvqBSURUQkYXiLi2l8+223y3BFx02MHb3a4q6PXq/g1cc3Ul/VTGZuKtM/N5EDjykY0jEXLlzIrFmzgj62dOnSfu8TT/sNlVovREQkYXhLivGXl2P9frdLibg9U8Op9cJ1H71ewXMPrKO+qhmA+qpmnntgHR+9XuFyZdKdRpRFRCRhpBQXQ3s7/oqKhBtZbfH5wOPBWzC0UUvp20tLPmJHaX2Pj3+6uZa2VttlW2tLOyv/vpYPX94W9Dl5JZmc+IUDQ1qn9E0jyiIikjC8Rc5oaiLOpewv9eEtKMAka4zMbd1Dcl/bxT36r0VERBJGSokTlFtKS8mYPt3laiLL7/PhTbBRdLf0NfJ73/df2dN20VlmbipnffvIcJUlg6ARZRERSRjJBQWQnIzfV+Z2KRHXUlamGS+ixPTPTSQ5pWsES05JYvrnJrpUkfREI8oiIpIwjMeDd8wY/L5St0uJqPbGRtp27NCqfFGiY3aLUM96IaFnrI2+fphp06bZ1atXu12GiIjEoa2XXEJbfQPjl/zL7VIipnnDBjbNP5Mxt9xC1vwz3C4nLq1du5bJkye7WsO5555LZWVl0McOP/xwbrvttn7t099jxcJ+3QX7ezLGvGmtnRZsf40oi4hIQvEWFdP07LNulxFRLXvmUNaIcjx76KGHQrJPPO03VOpRFhGRhOItKaGtqor2hga3S4mYjp5szaEcXtH4Kb3sNZi/HwVlERFJKCmBG9paEuiGPr/Ph0lPxzNypNulxK20tDR27typsBylrLXs3LmTtLS0AT1PrRciIpJQOqZI8/tKSZuUGAs4tPh8eIvGYIxxu5S4VVxcjM/n67FvVtyXlpZG8QA/VVFQFhGRhNLRfpBIi474fT5SijWHcjh5vV7Gjx/vdhkSYmq9EBGRhOLJziZp2LCEab2w1jqLjag/WWTAFJRFRCShGGPwlpTgL02MuZTbampob2jQYiMig6CgLCIiCcdbXIy/LDFaLzpaTLTYiMjAKSiLiEjCSSkupsVXlhAzFHQE5Y6bGEWk/xSURUQk4XiLi7GNjbTt3Ol2KWHXsdiIt0gjyiIDpaAsIiIJp6NftyUB+pT9vjI82dl4MjPcLkUk5igoi4hIwknZM5dy/M984S8t1YwXIoOkoCwiIgnHW+SMKCfCDX0tZT68JQrKIoOhoCwiIgknKT0dT35e3Lde2LY2/NvKNeOFyCApKIuISEJKKSqO+9aL1u3bwe/XjXwig6SgLCIiCclbXBz3i450jJirR1lkcBSURUQkIXlLivFXVGD9frdLCZuOEfMU9SiLDIqCsoiIJKSU4mJob8dfUeF2KWHj9/nAGLyFhW6XIhKTFJRFRCQheYsDU8TFcfuFv8xHckEBJiXF7VJEYpKCsoiIJKSUjkVHfPE7RVxLqY+UwFR4IjJwCsoiIpKQkgsKIDkZf2n8BmW/z4c3sLiKiAycgrKIiCQk4/HgHTMmbhcdaW9upnX79j3LdYvIwCkoi4hIwkopLqIlTudS9pdtA9BiIyJDoKAsIiIJy1tcErc38/l9mkNZZKgUlEVEJGF5i4tpq66mrb7B7VJCruMmxY7ZPURk4BSURUQkYXXMfBGPfcp+XxkmJYXk/Dy3SxGJWQrKIiKSsDpmhPDH4RRxfp8Pb1ERJkn/qxcZLP3XIyIiCaujfzceg3KLr1T9ySJDpKAsIiIJy5OdTVJGBi1xOJey31dGSomCsshQKCiLiEjCMsbgLS6OuxHltl27aN+1C2+RgrLIUCgoi4hIQvMWF9Pii68p4vx7ZrxQUBYZCgVlERFJaCnFxfjLtmGtdbuUkOloJdGqfCJDo6AsIiIJzVtcjG1spG3nTrdLCZmOEeWUEs2hLDIUCsoiIpLQvIEb3lriaIU+f5mPpBEj8IwY4XYpIjFNQVlERBJayp4p4spcriR0Wnw+tV2IhICCsoiIJDRvUWB1vji6oc9f6iNFM16IDJmCsoiIJLSk9HQ8+Xm0xMkUcba9HX9Z2Z5VB0Vk8BSURUQk4aUUFcdN60Vr5Q5sS4taL0RCQEFZREQSnrekBH+c3MznLwvMeKE5lEWGTEFZREQSnre4CH9FBdbvd7uUIesI/FpsRGToFJRFRCThpRSXQHs7/vJyt0sZso5e646bFEVk8BSURUQk4Xn3TBEX+zf0+X1lJI8aRVJqqtuliMQ8BWUREUl4KYEb3+Jh5gu/z6e2C5EQUVAWEZGEl1xQAMnJ+EtjPyhrsRGR0FFQFhGRhGc8HrxjxuyZMSJW2ZYWWisqnJ5rERkyBWURERGc6dRaYnxE2V9eDtaq9UIkRBSURUREcG7oi/Wb+fbMeKHWC5GQUFAWERHBCcpt1dW01Te4XcqgdfRYa7ERkdBQUBYREQFSSgJTxMVwn7K/zAdeL8mjR7tdikhcUFAWEREhPuZSbvH58I4pxHg8bpciEhcUlEVERNgblFsCS0DHIr+vjJQitV2IhIqCsoiICODJziYpIwO/r8ztUgbNX1qqGS9EQihsQdkYk2uMOc0Ykxeuc4iIiISKMcaZ+SJGR5Tb6htoq6nBW6KgLBIqYQnKxphCYDlwNPCcMSbfGHOvMWaVMeaH4TiniIjIUHlLimmJ0Zv5Om5C1IwXIqETrhHlQ4BrrbU/A1YAMwCPtfY4YIwx5oAwnVdERGTQUoqK8fvKsNa6XcqAdYyEq/VCJHSSw3FQa+0zAMaYk3BGlXOBJYGHVwInABvCcW4REZHB8paUYJuaaNuxg+T8fLfLGZC9i40oKIuESjh7lA1wHuAHDNBxd8QuYJ8JHo0xlxtjVhtjVldWVoarLBERkR51rGjXEoNTxPl9ZSRlZODJzna7FJG4EbagbB1XAauAY4H0wEOZwc5rrb3bWjvNWjstP8bexYuISHxIieG5lP0+H97iYpxxKhEJhXDdzPc9Y8yXAj9mAzfjtFsAHAZsCcd5RUREhsJb5Iwox2JQbvFpajiRUAtLjzJwN7DEGHMZ8AGwFHjRGDMGOB1nhFlERCSqJKWn48nPi7nWC2st/rJtZB5/vNuliMSVcN3MVw2c1nmbMeaUwLZfWWtrw3FeERGRoUopLsFfGltBuW3nTmxjI97iErdLEYkrEVuZz1pbba1dYq2tiNQ5RUREBspbXBxzrRf+PTNeFLlciUh80RLWIiIinXiLi/BXVGD9frdL6beWUi02IhIOCsoiIiKdpBSXQHs7/vJyt0vpt45V+XQzn0hoKSiLiIh04o3BKeJafD48eXkkpaf3vbOI9JuCsoiISCcpJU5QbomhG/r8vjJSitSfLBJqCsoiIiKdJI8eDV5vTI0o+0s1h7JIOCgoi4iIdGI8HryFhbT4St0upV9sayv+igq8JQrKIqGmoCwiItJNSnExfl9ZWI5du2wZG2bMZO3kg9kwYya1y5YN6Xj+igpoa9OMFyJhoKAsIiLSTbjmUq5dtozyG26kdds2sJbWbdsov+HGIYXlvXMoKyiLhJqCsoiISDfekmLaqqtpq28I6XG333obtqmpyzbb1MT2W28b9DFbSp0WEQVlkdBTUBYREemmo42hY37iUGntYW7mnrb3h99XBh4P3oKCQR9DRIJTUBYREelmz1zKpaG9oS+5hzCbNDwT294+qGP6fT68hYWY5OShlCYiQSgoi4iIdNMRlFtC3Kc8fM7sfTcmJdG+q47Sr11Ba1XVgI/p9/nUdiESJgrKIiIi3Xiys0nKyAj5zBctH23AjBhBcmEhGEPymDEU3nwzBYt+xO7XX2fzgrNoeOONgR3T58NbrMVGRMJBn9OIiIh0Y4zBW1IS0taL5k2baFi1ivxrribviiv2eTz98MMpu+Zatl78FfK+cRV5X/saxuPp9Zjtu3fTtnMnKcUlIatTRPbSiLKIiEgQ3uIiWkJ4M1/1/Q9gvF6yzz036ONpBx3E+EceZsQZ89jxu9+z9dLL8G/f3usx/WVlgVrVeiESDgrKIiIiQaQUOYuOWGuHfKy2+npqly5lxGdPJ3nkyB73S8rIYMwvf0nhz35G4zvvsPmss6l/5ZUe9+/ooU5R64VIWCgoi4iIBOEtKcE2NdG2Y8eQj1X72FLad+8m56KL+tzXGEP2OWcz/uGHSM7NofSyrzrzL7e27rOvv1SLjYiEk4KyiIhIEB03yA115gvb3k71Aw+QdthU0qdM6ffzUvffn3FLlpD9+XPYedddfPLli/F3m2/ZX+bDpKfj6WWUWkQGT0FZREQkiJQS5wa5oS5l3fDKKlq2bCH3wgsH/Nyk9HQKf/ITxtxyC81r17J5wVnUPffcnsdbfGWkFBdhjBlSjSISnIKyiIhIEN4iZ0R5qEG5+v778YwcyfC5cwd9jKz5ZzD+0UdIHjMG35Vf59Obf0nNY49R/8ILNG/4mA0zZlK7bNmQ6hSRfWl6OBERkSCS0tLw5OfRUjr4oNyydSv1L75I3pVXkJSSMqR6UsaNY9w/H2T7r26h6q9/BWMgcKNh67ZtlN9wIwBZ8+cP6TwispdGlEVERHqQUlwypBHl6n88CB4P2eedH5J6klJTKbjhh3hycvaE5A62qYntt94WkvOIiENBWUREpAfe4uJBB+X23bupefRRhp82C+/oUSGtq62mJuj21m43+4nI0Cgoi4iI9CClpBh/RQXW7x/wc2v/vYz2XbvI7ceUcAOVXFg4oO0iMjgKyiIiIj3wFhVDe/s+07L1xVpL9QMPkDp5MulHHhnyukZdew0mLa3LNpOWxqhrrwn5uUQSmYKyiIhIDzoW8mgpLR3Q83a/8T+aN2wg96ILwzJ1W9b8+RT+5CaSx4wBY0geM4bCn9ykG/lEQkyzXoiIiPQgpcQJyn5f2YCeV33//Xiyshgxb144ygKcsKxgLBJeGlEWERHpQfLo0eD14vf1f0TZv20bdc8+S/a5nyepW3uEiMQWBWUREZEeGI8H75jCAS1jXf3PfwGQ88UvhqssEYkQBWUREZFepBQV97v1or25mZqHHiJzxql7VvYTkdiloCwiItILb3Ex/n7ezLdr+X9pq64m98ILw1yViESCgrKIiEgvvCXFtNXU0FZf3+t+1lqq77+flP0nMuzYYyNUnYiEk4KyiIhIL1KKO2a+6L1PufGdd2has4bcC8MzJZyIRJ6CsoiISC+8xSVA30G5+v4HSMrMJOvMMyNRlohEgIKyiIhIL7zFzk15vc184d++nV0rVpB19lkkZWREqjQRCTMFZRERkV54srNJysjAX9pzUK5Z8hC0tpJ7wQURrExEwk1BWUREpBfGGLwlJT22XtiWFqr/9U8yTjqRlHHjIluciISVgrKIiEgfvMVFPbZe7Hrqadoqd5B70UURrkpEwk1BWUREpA8pxSX4y8qw1u7zWPX99+PdbywZJ5zgQmUiEk4KyiIiIn3wFhdjm5po27Gjy/bGDz6k8Z13yL3gAkyS/pcqEm/0X7WIiEgf9sx80e2GvuoHHsAMG0bW2We7UZaIhJmCsoiISB9SSgJzKZftDcqtVVXsWr6crM+diWf4cLdKE5EwUlAWERHpg7eoY0S5dM+2mocexra0aEo4kTimoCwiItKHpLQ0kvPz8fvKALCtrVT/858MO/ZYUg84wOXqRCRcFJRFRET6wVtcvGcu5bqVK2ktLyf3ogtdrkpEwklBWUREpB+8xcW0+JzWi+r7HyB5TCGZp57qclUiEk4KyiIiIv2QUlJMa8WnNK1Zw+433iDni1/EeDxulyUiYaSgLCIi0g/eomJob+fTW27BpKaS/fnPu12SiISZgrKIiEg/NJduBWD3q69BUhINL7/sckUiEm4KyiIiIn2oXbaM6r/8dc/PtrGR8htupHbZMveKEpGwU1AWERHpw/Zbb8M2N3fZZpua2H7rbe4UJCIRoaAsIiLSh9by8gFtF5H4oKAsIiLSh+TCwgFtF5H4oKAsIiLSh1HXXoNJS+uyzaSlMeraa9wpSEQiItntAkRERKJd1vz5gNOr3FpeTnJhIaOuvWbPdhGJTwrKIiIi/ZA1f76CsUiCUeuFiIiIiEgQCsoiIiIiIkEoKIuIiIiIBNFrUDbGJBljMnp57AvhKUtERERExF193cw3Dvi8MeZ/QE63xwzw/4AlYahLRERERMRVfQXlVqANuAF4CRgNnAS8BWwAbFirExERERFxSY9B2RiTDPwUGA4UAsuBA4BJwBvAK8BREahRRERERCTi+rqZ7yWgpdt+ttufIiIiIiJxp8cRZWttqzHmKSALyAd+D6TjjC4XAhcA2yNRpIiIiIhIpPXVo1wCvGOt/XX3B4wxSTjtGCIiIiIicae3HuVU4AdAkzFmRpBdkoCycBUmIiIiIuKm3lovmoHTjTETgJ8DU4FrgJ2BXQyQGu4CRURERETc0FfrBdbaTcD5xpjPA1uttevCX5aIiIiIiLv6DMrGmBKgAFgHtBpjioAmoMFa2xTm+kREREREXNFnUAYuB/YDmgFv4CsFyDfGbLDWfjWM9YmIiIiIuKI/QRngBmvtJ8aYTODb1tofG2MM4AtjbSIiIiIirul1wRFjzEE4C4tYY8zZwM9w2i8ustZaYH4EahQRERERibjepocbBvwaOAKoCXx9F2elvheMMe9Za9+KQI0iIiIiIhHX44iytXa3tfYM4ACgHrgSSAuMJF8JVEemRBGR0Fm+aTmzH57N1PumMvvh2SzfpHWTREQkuF5bL8AJzMAXgROttbXGGC/wY2ttadirExEJoeWblrNo1SLKG8qxWMobylm0apHCsoiIBNXrzXzGmFdwWi0OB/7r3L+HAaYYY8ZYa7eFvUIRkRBZ/NZimtq6zmrZ1NbE4rcWM2/CPJeqEhGRaNXXrBezgTZgJTAPaA9svwC4GGfFPhGRmFDRUDGg7SIikth6DcrW2gYAY8yPgaZAfzLGmKdwbvKTbpa+XcYtK9azraaRMdnpXDdnEguOKHK7LBEBCjIKKG8oD7pdRESkuz57lI0xqdbaFZ1CsheYY639T9irizFL3y7j+kffo6ymEQuU1TRy/aPvs/TtMrdLExHg6iOvJs2T1mVbmieNq4+82qWKREQkmvU1j7IHeNEY82PjuBj4FnBWJIqLFWU1jSxZXcr1j75Po7+9y2ON/jZuWbHepcpEpLN5E+ax6LhFe0aQ05PTWXTcIvUni4hIUH21XrQZYxqBjcACnHaL3wDHhr+06FXb6OfVjTt55eMdvPLxDjbtaOh1/201jRGqTET6Mm/CPOZNmMcPXv4Bz219jtP2O83tkkREJEr1ZwlrC5QBL+OMJP86sC2u9NZb3Nzaxluf1PDyx5W8/PFO3vfV0G5hWIqHYyeM5MJj9+OE/fO45K9vUFbTtM+xx2SnR/rXEZE+nD7+dP698d+8UvYKp4491e1yREQkCvU1Pdx5OKG4BPgnzmjyd4BCY8wXgGRr7T/CXmWYOb3F79PobwOcVorvPfIeK9d9Sk1jK29s3kmTvx1PkuHwkmy+MeMATtg/j8NLsklJ3tu9ct2cg7ocByDd6+G6OZMi/juJSO+OKTyG7NRsntzypIKyiIgE1deI8mhgLDABZ4W+y4HhQBpQCKSGtboIuWXF+i7hFqC5tZ1/v1vOAaMyOf8zYzlh/zyOmZDL8DRvj8fpGIG+ZcV6ymoaSU4y/OLsKZr1QiQKeZO8zNpvFss3LaextZH0ZH3yIyIiXfV6M5+19ndAKbAJaADuBGqBjdbaxdbaXwV7njEmyxjzhDHmaWPMY8aYFGPMvcaYVcaYH4b6lxiqnnqIDfD0t05m0ZmHMOvg0b2G5A4LjijilYUz+OG8ybS2W47aLyfE1YpIqJw+7nQaWxt50fei26WIiEgU6nN6uMA+lcCXgTOAS/vxnAuB31prTwMqgPMBj7X2OGCMMeaAQdYbFj31EA+lt3jm5NEAPLv200EfQ0TC66jRR5GXnseKLSvcLkVERKJQX9PDJQPpwNHAZmAJzmp8vSZIa+0frbVPB37MBy4KPBecVf5OCHKuy40xq40xqysrKwf0SwzVdXMmke71dNk21N7i8XkZTMjP4Nl124danoiEiSfJw+z9ZvOi70Ua/L3PXiMiIomnr9aLVmvt0dbaH1lrm6217wDfY2/o7ZUxZjqQg9O+0bHqxi6c3ufu57rbWjvNWjstPz9/IL/DkC04oohfnD2Foux0DFCUnR6S3uJZk0fz2qad1DX5Q1OoiITc3PFzaW5r5rnS59wuRUREokxfs14cDhwOtHd7qM0Y8yXgQ2vtmz08Nxf4PXAOziIlHaPQmfSv5SOiFhxRFPKb7mZNHs3dL27ipQ07+OyUwpAeW0RC47D8wyjIKODJzU9yxoQz3C5HRESiSF+BdR7O7BatQFunPzMCj/8p2JOMMSk4o87XW2s/Ad5kb7vFYcCWIVUdI44cm032MC/PrFGfski0SjJJzNlvDq9se4Xa5lq3yxERkSjSV1C2wH+BzwP/wFmZbwlwnrX2b8D1PTzvUuAo4AfGmOdxJpD4f8aY3wJfAJYPvfTol+xJ4tRJo3hu/Xba2uNujRaRuHH6+NNpbW9l5daVbpciIiJRpK+gnBz4Gg7cizObxZ+AAwGstUFvFbfW3mGtzbHWnhL4ug84BXgNONVamzDDNjMnj6J6t5+3tla7XYqI9ODgkQdTnFnMk1uedLsUERGJIn0F5T8C6wL7XY8z88VCnHmVB8RaW22tXWKtrRhwlTHspAPzSU4yPKNp4kSiljGG08efzuvlr1PVVOV2OSIiEiV6DMrGmHzgXzgtFLfjzKX8deBT4FHj6HsFjgQ3Is3LsRNG8uxaTRMnEs3mjJtDm23jmU+ecbsUERGJEr2NKO8ALgbWAFcDTwK/BlYApwNPAU/39GTZa+bkUXy8vZ4tOzRPq0i0OjDnQCZkTeCJzU+4XYqIiESJHoOytdbiTOX2eWvtycAtgZX27ga+b609zVp7SmTKjG2zAqv0qf1CJHoZY5g7bi5vfvom23frEyAREem7R/kG4BVjzFzgu4FtG4GLjTFvGGOuDGt1caIkdxgHjs5U+4VIlJszfg4Wy1NbnnK7FBERiQJ9BeWLrLXv48x8cSGAtfZta+1VwGeBDWGuL27MnDya/22porZRq/SJRKsJWROYlDNJs1+IiAjQd1B+wxizCvgBsNQYs6rjC1gGXB72CuPErMmjaG23vPBRpduliEgv5o6fy7uV71JWX+Z2KTFr+ablzH54NlPvm8rsh2ezfFNCTJ0vInGo16BsrZ0GnAz8H/DNTl9XW2unA7vCXmGcOLwkh5EZKTyrPmWRqDZn3BwAVmwJOk289GH5puUsWrWI8oZyLJbyhnIWrVqksCwiMamvEWVwlqu+DPhy4Oti4KsA1trLwlZZnPEkGU49aBTPrduOv63d7XJEpAclw0uYkjeFJzer/WIwFr+1mKa2pi7bmtqaWPzWYpcqEhEZvF6DsjFmvrW2BrgCSLfW/h/wO+B7Eagt7syaPIpdTa2s3qJV+kSi2Zxxc1hbtZZPdn3idikxw9/u55GPHqG8oTzo4xUNCbXWlIjEib5GlO8wxuQHporLNsZMBu4CcsJfWvw58YB8UjxJar8QiXId7RcaVe5ba3srj214jPmPzWfRq4vwJgVfhyrJJPF+5fsRrk5EZGj6Csr/Bp4wxjyH06v8IjAcuMcY85wx5sVwFxhPMlKTOXbiSJ5dp2niRKJZQUYBR446UrNf9KK1vZXHP36cM5eeyY2rbiQrNYvbZ97OTcfdRJonrcu+KUkpZHgz+H9P/D/ufPdOWttbXapaRGRg+grKK4BngbnA2zgr8zXitF7MDXzJAJw2eRSbdzSwsbLe7VJEpBdzx8/l45qP2VCtWTA7a21vZdnGZSx4fAE/fOWHZHoz+f2M3/PPef/kpOKTOGPiGSw6bhGFGYUYDIUZhdx0/E08cc4TzBk3h9vfuZ2Ln7yY0l2lbv8qIiJ9Mk5XRQ8PGjMB+IG19lJjzGPW2rOMMfsBfwd+Y619PBxFTZs2za5evToch3ZdWU0jx9+8ku9/9iAuP2mi2+WISA92NO5g5kMzuWzKZXzziG+6XY7r2trbeGLLE9z17l1s2bWFSTmT+PrhX+fUklMxxvT7OP/d9F9++tpPabNtLDx6IQv2XzCg54uIhJox5s3ATG/76Gt6uE3W2ksDPy4MbPsEOAPQXS6DUJSdzuTCETyzRu0XItEsLz2PzxR8hhVbVtDbgEK8a2tv47+b/stZ/z6L61+6Hq/Hy62n3MqS+UuYMXbGgEPuZyd8lkfOfIRD8g7hxlU38q3nv0V1k25wFpHolNzfHa216zt9vwt4JxwFJYJZk0dx+3MfU93QQk5GitvliEgPTh93OoteXcTaqrUcPPJgt8sJu+WblrP4rcVUNFRQkFHAKcWn8EbFG2ys3cj+2fvzm5N/w6z9ZpFk+jOzaM8KMwu5Z/Y9/O3Dv7H47cW8++93+cnxP+H4ouND9JuIiITG0P61k0GZOXk07Rae/0ijyiLRbNZ+s0g2yQlxU1+whUIeXP8gdS113HLyLTxy5iPMHjd7yCG5Q5JJ4uJDL+bBeQ+SlZrFFc9cwc1v3ExTa1PfTxYRiRAFZRdMLcoif3gqz6xVUBaJZlmpWUwfM50Vm+O7/cLf7udX//vVPguFAHiSPMwdNzdkAbm7g3IP4sF5D3LR5It4YO0DnP+f81m7c21YziUiMlAKyi5ISjLMPGgUL66vpKVVq/SJRLO54+eyrWEb7+14z+1SQmq3fzfPfPIM1790PSf/62SqmqqC7heJhULSktP43tHf465Zd7GrZRcX/PcC/vzBn1n28TJmPzybqfdNZfbDs7UMtohEXL97lCW0Zk4ezT//V8r/tlRx/P55bpcjIj04teRUUpJSeHLzkxyWf5jb5QxJTVMNz/ue59mtz/LqtldpbmsmKzWLGSUzeNH3ItXN+95UV5BRELH6jis6jkfPfJQfv/pjbn3zVpJIoh1nMKG8oZxFqxYBMG/CvIjVJCKJTUHZJSfsn0dqchJPr/lUQVkkig1PGc4JRSewYssKvjPtO3iSPG6XtI/uN+FdfeTVe8JkRUMFz259lpVbV/Lmp2/SZtsYPWw05xxwDjPHzuTI0UeSnJS8p0e5c/tFmieNq4+8OqK/S3ZaNr895bec+K8TqW2u7fJYU1sTi99arKAsIhGjoOyS9BQPx++fx7PrPuVH8w/WPKIiUez08aezsnQlb21/i88UfMbtcrroHnDLG8r50aof8dzW5/DV+/hw54cATMiawCWHXsLMsTM5eOS+/+Z0hM+eAnckGWPY1bwr6GORaAUREemgoOyimZNHsXLddjZsr+fA0cPdLkdEenBS8UmkJ6fz5OYnoy4oL35r8T434TW3NbPikxVMyZvC1UdezcyxMxmfNb7PY82bMC9qRmsLMgoobygPul1EJFJ0M5+LZh40GoBn1n7qciUi0pth3mGcXHwyT3/yNK3trW6X00VPI6wGwz/m/YPLplzWr5Acba4+8mrSPGldtrnRCiISCh+9XsF933+F269YyX3ff4WPXtcnI7FCQdlFBVlpTCnK4llNEycS9eaOm0t1czVvlL/hdild5A/LD7o91kde502Yx6LjFlGYUYjBUJhRyKLjFkXNiLdIf330egXPPbCO+qpmAOqrmnnugXUKyzFCrRcumzl5FIuf3cDO+mZGZqa6XY6I9OCE4hPI8Gbw5JYnOa7oOLfLAWBb/TZa2lr22R4vI6/R1AoiMhhNDX5efmgDrS1dp4JtbWnn1cc3cuAxsf2GNhEoKLts1uTR3PbMBlau286500rcLkdEepDqSWVGyQye2foMNxx7A16P19V6fHU+Ll1xKW3tbXz9sK/z2MePuX4Tnkgis9ZSu72Rik21lG90vqrLG3rcv2OEOZZ99HoFrz6+kfqqZjJzU5n+uYlxF/4VlF12yJgRFIxI49m1Csoi0W7u+Lks27SMVdtWcXLJya7VUbqrlEufupQGfwN/mvMnDhl5CFcefqVr9YjEq96CYKu/jcpP6ijfVEvFxloqNtXSWOcHICU9mYIJIzjwM6N47znfnu2dZebG9qfIHS0lHaPlHS0lQFyFZQVllxljmDF5FI+/XUZzaxupydE3R6uIOKYXTmdEygie2PKEa0H5k12fcOmKS2lua+ae2fcweeRkV+oQiXfBguCzf1/L+v9V0LK7le1b62hvdZa2z8pPZ+whIymcmEXBhCxyCzMwSc4UjCNGpnc5DkByShLTPzcx8r9UCL36+MaEaClRUI4Cp00ezT9e38prm6o4+cDgN+aIiPu8Hi+n7XcaT2x+gqbWJtKS0/p+Ughtrt3MZSsuw9/u557Z9zApd1JEzy+SSIIFwfZWy9YPqiiYkMXUU0v2BONhI1J6PE5HaHzpoQ001fsZNiKF48/ZP+bDZE+tI/HQUtKZZr2IAtMnjiTd6+FZTRMnEvXmjp/L7tbdvFT2UkTPu7FmI5esuIRW28q9c+5VSBYJs94C3znfPYrjz9mfCYfn9xqSOxx4TAHzv3kYACdfMCnmQzL03DoS6y0l3SkoR4E0r4cTDsjj2bXbsda6XY6I9GLa6GnkpuXy5OYnI3bODdUbuGTFJQD8Zc5fOCDngIidWyTR1FU18cSd7/f4+GCDYGaO8wlUfXV8jLhO/9xEkjzdVhU28JnPjnOlnnBRUI4SsyaPoqymkbXldW6XIiK9SE5KZvZ+s3nR9yK7/bvDfr71Veu5dMWlJJtk/jznz0zInhD2c4okorbWdt58cgv/WPQaWz/cycSj8kn2do1JQ+ktTs/0kpRsaKhp6nvnGHDgMQXkjskgKdCLnT7cCxY2vr2D9rb2Pp4dO9SjHCVOPWgUAM+u/ZSDx4xwuRoR6c2I1BE0tTVxzD+OoTCjMGzTsa3duZavPv1V0jxp/HnOnxk7YmzIzyEi4FtfzYsPrqe6YjfjD8vjhHMPYEReekinPzNJhszsVOriqIe3qd7PxKNGMfvSQwD44MUyXvjHel5esoETzz8QY0wfR4h+CspRYtTwNA4vyeaZddv55kx9rCoSrZZvWs7fPvzbnp/LG8pZtGoRQEjD8oc7P+Typy4nw5vBvXPupWS4po8UCbWG2mZeefhjNvzvU0bkpTHvqqmMm5K35/EDjykIaT9xZk4a9dXxMaLcWNdCfXUz+SXD92w79KQiarfv5p1nSskaNYzDZsb+v1sKylFk1uRR/Pqpj9he18So4ZG9m15E+mfxW4tpauv6P7qmtiYWv7U4ZEH5/cr3+drTX2NE6gjunXMvRZlFITmuiDja29p5//kyXl+2ifZWy7R54zhqzn4kp4R3itbMnFQqNtWG9RyRUlnqtIrmj83ssn362ftTW9nIyw9vYER+OuOn5gV7esxQj3IUmTl5NADPrdvuciUi0pOKhoqg28sbyrnj3Tt4e/vb+Nv3XVygv97Z/g6XP305WalZ/GXOXxSSRUKs/OMalvx8NS8/tIHCiVmcf+PRHDN/QthDMjhBub66Gdse+zfuV251gnJepxFlgKQkw2mXHEJ+yXCeuvfDPfvFKgXlKHJQwXCKstN5Zq2Cski0KsgI/jFsclIyd7xzB1964kuc8OAJXPXsVfx9zd/5qPqjfs9m89anb/G1p79Gblouf5n7FwozC0NZukhC272rhWfvW8Ojv36L5t1+5n7tUM74xmFkjxoWsRoyc9Job7M01g/+zXS02FFaz4i8NNIyvPs85k31MO+qqaQNS2b57e/GdLuJWi+iiDGGmZNHsWR1KU3+NtK8WqVPJNpcfeTVLFq1qEv7RZonjUXHLeKEohN4o+INXi9/ndfKX+NF34sAjEwbydGFRzO9cDrHFB7DmMwxgNPvvPitxVQ0VJCblsuu5l0UDS/i3jn3MmrYKFd+P5F40P0mvOJJOWx+dwf+pjaOnDOWaZ8djzc18v+Pzch2pparr27q1/zL0axya12X/uTuMrJSmXfVYTz66zdZ/sf3OOvbR5KSFnuxM/YqjnMzJ4/mb69+wqqNO5hx0Gi3yxGRbjr6kDsCbkFGQZdZL07b7zRO2+80AMrry3mt/DVer3id17a9xhObnwBg7PCxFGQUdGnT2Nm0E4PhgoMuUEgWGYJgS0+ve7WC7IJ0zr7uKHILM1yrbXju3rmUR+3nWhlD1tzYSm1lIwcd1/unXnnFmcy57FCW3/4uT9/7IadfOXXPdHKxQkE5yhw7IZeMFA/PrN2uoCwSpeZNmNevG/cKMws564CzOOuAs7DWsrFmoxOcy1/nBd8LWLq2ZFgsf/nwL3xx8hfDVbpI3Au29DRAa0u7qyEZnB5lIKZbEQB2dNzI18uIcof9Dh3JiecdyIv//IhXHtrAiecdGO7yQko9ylEmNdnDSQfms1Kr9InEFWMM++fsz0UHX8TvZ/6+x/16ullQRPqnp6Wne1uSOlLSMr14kpNifnW+jhv08sf2HZQBppxSzGEzSnjvOR/vPecLZ2khp6AchWZOHk3FriY+3LbL7VJEJEx6uimwp+0i0j89LTE92KWnQ8kYQ0Zg5otYtqO0noyslAH1WR/3+f0ZNzWPl5d8xJb3d4SxutBSUI5Cp07Kxxh4Zu2nbpciImFy9ZFXk+bpOl96mieNq4+82qWKROLD9M9NJDkldEtPh1pmdmrMt15Ultb1ezS5gzNt3MGMLM7kqXs+ZIcvNqaNU1COQiMzU9kvdxi3P/cx4xcu5/ibV7L07TK3yxKREJo3YR6LjltEYUYhBkNhRiGLjlsUlqWwRRLJgccUcOqFB+0ZQc7MTeXUCw8K6Qp7Q5GZG9sjyv6WNqrLG8gbYFAGSElLZt7XDyMlPZnlt79HQ030XwfdzBeFlr5dhq+6kdbAhORlNY1c/+j7ACw4QosPiMSL/t4UKCIDE+qlp0MpMzuNhprt2HaLibEZIAB2+uqxtn838gWTmZPKvKum8uiv39ozbZwbU/X1l0aUo9AtK9bvCckdGv1t3LJivUsViYiISChk5qTS3mbZXdfidimDMtAb+YLJLxnOnMsOYUdpHU/d+yHtUbxSoUaUo9C2msYBbRcREZHYsHeKuGYysty/wXCgKkvrSMvw7vk9BmvclDxO+MIBvPSvDfznD+9SXdGwZ4GY6Z+bGDWfCGhEOQqNyU4f0HYRERGJDZk5zk28DTHap7yjtJ78sZkYM/S2kamnllA8OYfSNVV7pu+rr2rmuQfW8dHr0TFVpoJyFLpuziTSuy1fne5N4ro5k1yqSEREREKhYyS2LgZnvmhrbWdnWf2Q2i66q/l09z7bWlvaefXxjSE7x1Co9SIKddywd8uK9ZQF2i0uOWG8buQTERGJcR2LjsTiiHLVtgba2yx5g7yRL5hoXiAGFJSj1oIjilhwRBFN/jZO+OVzfFCmxUdERERi3d5FR2JvRLmydOg38nWXmZsaNBRHwwIxoNaLqJfm9fCV48fxwkeVfLit1u1yREREZIiG56RSHwNzCHdXubUOb5qHrLzQ3TMV7QvEKCjHgIuO3Y/M1GTufGGT26WIiIjIEGXmpEVNa8FAVG6tI79keEjnf472BWLUehEDstK9XHjsWP704iaumz2JsSOHuV2SiIiIDFJGTioNNc20t1uSYmTRkfZ2y05fPYecGPr7paJ5gRiNKMeIS48fT3JSEne/FB13gYqIiMjgDM9Jpb3d0hhDi47UVOym1d9O/thMt0uJKAXlGDFqRBrnHFXEktU+Kuti7+MaERERcWQE5lKOpfaLjhv58kJ4I18sUFCOIZefNBF/Wzt/XbXZ7VJERERkkPaszlcTOzNfVG6tI9mbRM7oxGr/VFCOIePzMjj90AL+9uon1DX53S5HREREBmFPUI6lEeWtdYwsziTJk1jRMbF+2zhwxckTqWtq5cE3tkb0vEvfLuP4m1cyfuFyjr95JUvfLovo+UVEROJFWoYXjzcpZqaIs+2WHaXOjBeJRkE5xkwtzub4/Udyz0ubaW5ti8g5l75dxvWPvk9ZTSMWKKtp5PpH31dYFhERGQRjDJnZsbPoyK6djbQ0tYV0oZFYoaAcg648eX+21zXz2FuRCaq3rFhPo79rKG/0t/HDpR/w2Ns+1mzbFbHQLiIiEg8yc1NjZhnryq31QGhX5IsVmkc5Bh2//0imFGVx94ubOHdaCZ4wz8G4raYx6Pb65lau/de7ACQnGSbmZ3JQ4XAOKhjBQYXDmVwwgtEjUjHGqW/p22XcsmI922oaGZOdznVzJrHgiNDPxygiIhLtMrPTKNtQ7XYZ/VK5tY4kjyG3MMPtUiJOQTkGGWO44uSJXPWPt3jqwwpOn1IYtnPVN7fiSTK0ttt9HhuTncZ9XzmatRV1rCvfxbqKOv63uYrH39m2Z5+sdC8HFQwnJTmJ1zbtxN/mHKejfQNQWBYRkYSTmZPK7pqWmFh0pLK0jtwxGXi8ideIoKAco+YeWsC4kcO444WNzD20YM+obShZa/n+o+/T2m5JSU6ipbV9z2PpXg/fnXMQB4wezgGjh3PmYWP2PFa728/6T+tYV7GLteXOn29srqJ71G70t3HLivUKyiIiknAyOxYd2dVCRnaq2+X0yFpL5dY6xk/Nc7sUVygoxyhPkuHykyby/cfe59WNOzlu/9C/gP/xxlb+/e42rpsziaLs9H63TWQN83L0+FyOHp+7Z9v4hcuD7ttTW4eIiEg8ywwsOlJX3RTVQbm+upmmen9C9ieDgnJMO/vIIm595iPueGFjyIPyB2W1/HjZGk4+MJ8rT55IUpIZ0sjvmOx0yoKE4jHZ6UMpU0REJCZl5jrhuKG6Gca7XEwvdgRW5EvUoJx4zSZxJM3r4dITxvPShh18UFYbsuPuavJz1T/eIndYCreed3hIeqeumzOJdK+nyzZPkuG6OZOGfGwREZFYk5kdWMY6yme+qNxahzEwsijT7VJcoaAc4y44ZizDU5O544WNITmetZbvPfwevupG/nDBEeRmpITkuAuOKOIXZ0+hKDsdAwxL8YC1HB+GlhEREZFol5qRTLI3KernUq4srSe7IANvqqfvneOQgnKMG5Hm5aLp+/HE++Vs2dEw5OPdt2oLT3xQwffmTmLauNy+nzAAC44o4pWFM9h88zz+880TaAf+umpzSM8hIiISC4wxZOSkxsSIcv7YxBxNBgXluPCV48eR7Eni7pc2Dek475bW8LP/rmXW5FF89cQJIaouuAn5mZx+aAF/e/UT6pr8YT2XiIhINMrMSYvqoLx7VwsNNc0JuXR1BwXlODBqeBqfP6qYh1f72L5rcB/h1O728/UH3mLU8DR+fe5hYZlurrsrTp5IXVMr/3h9a9jPJSIiEm0yc6J7Ges9N/IpKEusu/zECbS2t/PnV7YM+LnWWr790Ltsr2viDxccQfaw0PQl92VqcTYn7J/HPS9vpsmvJbBFRCSxZOak0lDrLDoSjSoDQTmvRK0XEuPG5WVw+pRCHnjtE3YNsJXhnpc288zaT7n+9MkcMTYnTBUG9/VTJlJZ18yjb5VF9LwiIiJuy8xJw7Zbdte2uF1KUJVb6xiRn07qMK/bpbhGQTmOXHnyROqaW3ngtf63Mrz5STW/fHIdcw8p4CvHjwtfcT2YPnEkhxVncdeLG2mL0nfUIiIi4ZCZ48ylXF8Tne0XlVvrErrtAhSU48qhRVmceEAef36lf60MVQ0tfOMfbzEmO51fnTs1In3J3RljuPKUiXyyczdPfFAe8fOLiIi4ZU9Qroq+G/qaGvzs2tGU0DNegIJy3Lny5P61MrS3W7615B121rfwxwuPZESaex+rzD64gAn5Gdzx/Eas1aiyiIgkho5lrKPxhr4dvnogsW/kAwXluNPRynB3H60Md764kefXV3LD/IM5tCgrghXuKynJcMVJE/lw2y5e3LDD1VpEREQiJXVYYNGRmugbUd6x50Y+BWWJI8YYrjh5Ilt27ubJDyqC7vP6pp38esV6zphayEXHjI1whcEtOKKIghFp3PH8x26XIiIiEhHGGDJz06Ky9aJyax2ZOakMGxGZmbCilYJyHJp9SAET8jK444WP92ll2FHfzDcffJv9Rmbwi7OnuNKXHExKchKXnTie1zZV8dbWarfLERERiYjMnFQaovBmvsqtdQk/mgwKynHJk2T42skT+KBsFy9/vLeVoa3dcs0/36G20c/tFxzJcBf7koP54tFjyUr3cufzG90uRUREJCIys6NvGWt/cxvVn+4mf6yCsoJynFpwRBGjR6Ry5wt7Q+cfVn7Myx/v4MdnHsLBY0a4WF1wGanJfPm4cTy15lM+3l7ndjkiIiJhl5mb5iw60tbudil77PDVg4X8BF5opIOCcpxKTfZw6QnjeeXjnXzmZ88wbuFybn3mI6btl815nylxu7weXXzcONK9Hu54fpPbpYiIiIRdRnaqs+jIruhZdKRya2Dpao0oKyjHsxFpyQBU1u39SOeDbbt4/J1tbpXUp9yMFM4/uoTH3ymjrKbR7XJERETCas9cylHUfrGjtI704V4yslPdLsV1Cspx7Pcr9+31bfK3c8uK9S5U03+XnTgBgHte0qiyiIjEt71zKUdPUK4sdVbki5Yb/t0UtqBsjBltjHkp8L3XGPMfY8wqY8wl4TqndLWthxHZnrZHi6LsdD53eBH/fKOUqobo+ShKREQk1PaOKEfHzBdt/naqyhrIU9sFEKagbIzJAe4DMgKbvgmsttYeB5xhjNHVj4Ax2ekD2h5Nrjh5Ao3+Nu5btcXtUkRERMImdVgyySlJUTOivHNbPe3tNuFX5OsQrhHlNuA8YFfg51OAJYHvVwHTwnRe6eS6OZNI93q6bEv3erhuziSXKuq/A0YP57SDR/PXVVtoaG51uxwREZGwMMaQmZMWNSPKe2/k04wXEKagbK3dZa2t7bQpAygLfL8LGN39OcaYy40xq40xqysrK8NRVsJZcEQRvzh7CkXZ6RicloZfnD2FBUcUuV1av1x5ykRqG/08+MZWt0sREREJm8yc6JlLubK0npT0ZEbkRf+nz5GQHKHz1APpQC2QGfi5C2vt3cDdANOmTbPdH5fBWXBEUcwE4+6OHJvDsRNyueelzXxp+jhSknXvqYiIxJ/MnFRK10bHqrQ7SuvIL8nUjXwBkUoebwInBL4/DNgSofNKjLvylP2p2NXE0nfK+t5ZREQkBmXmpLG7ttn1RUfa29rZ4avXjXydRCoo3wf82BizGDgYeD1C55UYd9IBeRxcOII7X9hIe7s+aBARkfiTmZOKtdBQ6+5MT9UVu2nzt+tGvk7CGpSttacE/vwEOA14BZhlrW0L53klfhhjuPKUiWyqbOCpNRVulyMiIhJyHXMpN9S426dcWaoV+bqLWNOntXabtXZJt5v8RPr02SmF7DdyGHc8vxFrNaosIiLxpWMu5boqd2e+qNxaR3JKEtmjh7laRzTR3VES9TxJhq+dNJF3fbW8unGn2+WEz3tL4NZDYVG28+d7S/p8ioiIxL6OoOz6iPLWOvKKM0lK0o18HRSUJSacfWQR+cNTueOFfZflHrRQBdNQHOe9JbDs/6C2FLDOn8v+T2FZRCQBpKQnk5zqob7KvaBs2y07fPXqT+4mUtPDiQxJmtfDpSeM5+Yn1vG+r5YpxVlDO2BHMPUHlvPuCKYAU78QnuO07Ib6T6Gh0vmzfrvz1bAd3nkQWrstLe5vhGdvGlg90ei9Jc7vUeuDrGKYeWPs/04iIiFkjGF4Tqqri47UVjbib2rTjBfdKChLzLjwmLHc/tzH3PHCx/zxwqOGdrBnb9obbjv4G2HplfDq7eAdBt70wNcw8KZ12tbpsZU/C36c/1wLa5ftDcL126Fln+nDHcNG7huSO9T6hvZ7ui1Ub0hEROJcRnYq9S62XuhGvuAUlCVmDE/z8qXp+/HH5zeyqbKeCflDWF6zpwDa3gqZo8G/G5pqoa7C+b61yfnT3wht/Zi+p6UeKtdD5igYcwRkjHK+zxwd+HOUsy0jDzxep2WjtnTf42QVD/53jAY9vSGJh5FyEZEQysxNo/RD9+7DqdxaR1KyIbcww7UaopGCssSUi48bzz0vbebuFzdx8zlTB3+grOIegmkJXNhHX3BbqzMC7G+Eu06CuvLgx/nGG/2vZ+aNXUdeAZLTnO2xytrg1xic7c/9Ag6aBwVTQCtAiUiCy8xOpWFXC21t7Xg8kb+FrHJrHSPHZOLRKrhd6GpITMkfnsq0/XL45/9KGb9wOcffvJKlbw9i1b6ZNzqtE5150/sXTD3JkDrcGRU+7abBH6ezqV+A+b9zAjaB0HjAnNgddW3aBQ9f0vPjnhR44Zdw14lw21R44nuw6QVo80euRhGRKJKZkwoWdruw6Ii1lh2l9eSXDOGT2jilEWWJKUvfLmP1J9UAWKCsppHrH30fgAVHFPX/QB0BdKg3mYXqOB3H6nje/Z+H0tegtRmSUwd+LDeVvwcPfRmqP4GDz4YNT3QdKfemO28KJpwKHz0B6/4Lb/4VXr8T0rLhwDnOSPPEmZDa6R9t3RQoInGsY9GR+upmhuemRfTc9dXNNDX41Z8chIKyxJRbVqynubW9y7ZGfxu3rFg/sKAMXYPpUITqOJ0deyXcfzZ88Cgc/sXQHjtcrIU3/wJPLIRhuXDxf2C/43oPuEd+yflqaYCNK2HdcvjoSXjvX+BJhQmnOKG5zQ9P/1A3BYpI3OqYS9mZ+WKIMzsNUOVW50Y+zXixLwVliSnbaoLPDtHT9pg1cQbkHwSv/REOOz/6e3ib62DZNfDBw85I8Nl3OzcqQv/eSKRkwOT5zldbK2x9Fdb/F9b9BzasCP4c3RQoInFkb1CO/MwXlVvrMEmGvCK1XnSnHmWJKWOy0we0PWYZ44wqV7wHn6xyu5reVbwPd50MHz7qjBZf+PDekDwYnmQYfyLM/QVc/R5c8XLP+8b69HkiIgEp6cl4Uz2uzKVcWVpHTsEwklM8ET93tFNQlphy3ZxJpHu7/oec7vVw3ZxJLlUURlPPg/RcZ1Q5GlkLq/8Cf5rpTJ335f/Aid+GpBD+s2KMMytGVknwx2N9+jwRkQBjDJk5qTS4NKKsFfmCU1CWmLLgiCJ+cfYUirLTMUBRdjq/OHvKwPuTY4E3HaZ9xenbrdrsdjVdNdfBI5fBf66BccfD115y/gyXocxSIiISIzJzUqmLcFBuqG1md22LbuTrgXqUJeYsOKIoPoNxMJ+5DF5ZDG/8Ceb+3O1qHBUfOLNaVG2CGT+EE0I8ihxMKGcXERGJUpk5aezcFtlFR3aUOqvG5o9Vf3IwCsoi0WzEGDjkLHjrb3DKQkgb4V4t1jp1PPFdZxq3Ly+DcSdE7vzhmF1ERCSKZOSksjvCi47smfGiWCPKwSgoi0S7Y6+E9x+Cdx5wvo+UztO6jRgDI4rB97oz//HZf4LM/MjVIiKSAIbnpIGFhppmRoyMzE3qlaV1ZI1KJyVdkTAY9SiLRLuio6DkWGdBjva2yJzzvSXOPMW1pYCFXWVOSD54AVz0qEKyiEgYZASmiIvkDX2VW+vUn9wLBWWRWHDslVC9xVmMIxKevanranodyt4Mfz+yiEiCivRcyk0Nfup2NmnGi17o/3giseCgM5wp0l67IzLn62l+Ys1bLCISNp2XsY6EHaVOf7KCcs8UlEVigScZjr4ctrwE5e+F/3wjxgTfrnmLRUTCJjU9GW9a5BYdqdzqzHiRpxkveqSgLBIrjvx/4M2IzKhyzrh9t2neYhGRsMvMTqW+JjIjypWldWTmppKemRKR88UiBWWRWJGeA4dfAB88DHWfhu88a/4Nn7wCkz4bWBHPOH/O/52mZxMRCbPM3DTqqyI1oqwV+fqiuUBEYsmxV8L//gSr/wynXh/64+8qd2a7GHMEfOFv4PGG/hwiItKjzOxUdpbVh/08LU2t1GzfzYFHjw77uWKZRpRFYsnIiXDgXFh9L/hDPOLQ3g6Pf9057tl/UkgWEXFBZseiI63tYT3PDl89WN3I1xcFZZFYc+yV0FAJHzwS2uO+cTdsXAlzfgZ5B4T22CIi0i+ZuXsXHQmXj16v4L9/dG4Mf/4f6/no9YqwnSvWKSiLxJrxJ8Oog52b+qwNzTG3r4Wnb3RGq6ddEppjiojIgGVmB+ZSDlNQ/uj1Cp57YB3Nu1sBJ5A/98A6heUeKCiLxBpjnFHlT9+HLS8P/XitzfDIVyFtBJz5B+f4IiLiir1zKYfnhr5XH99Ia0vXto7WlnZefXxjWM4X6xSURWLRlHNh2Eh47Y9DP9bKnzqh+8w/aGlqERGXhXt1vvqq4MftaXuiU1AWiUXedJh2Kax/AnYOYRRg84uw6vdOu8WkuaGrT0REBiUlPZmUNE/YgnJmbuqAtic6BWWRWPWZSyEp2bkJbzAaq+GxK2Dk/jD7Z6GtTUREBi0jJ42GMAXl6Z+biOmW/pJTkpj+uYlhOV+sU1AWiVXDC+DQc+Dt+6GpdmDPtRb+8y2o/xTOvhtShoWnRhERGbDhOalh61E+4OjRpKR5SPY6ETAzN5VTLzyIA48pCMv5Yp0WHBGJZcdeAe/90wnL06/q//Pefwg+fBRm3ABFR4avPhERGbCMnFRnnuMwqPl0N8272zjlwkkccmJRWM4RTzSiLBLLxhwBY4+D1++E9rb+Paf6E1j+bSg5Fk64Nrz1iYjIgGXmpLG7LjyLjmxdUwVAyeTckB87Hikoi8S6Y6+Emq2wbnnf+7a3OX3J1sLZd0GSJ/z1iYjIgGTmpIZt0ZHStVVkjUpnRF56yI8djxSURWLdQfMge6yzAElfXlkMW1fBvF9DzriwlyYiIgMXrini2vztlK2vZqxGk/tNQVkk1iV54JgrnAC87e2e99v2Djz3MzjkLJh6XsTKExGRgcnMDs+iIxWbamltaafkYAXl/lJQFokHR1wEKZnw2p3BH2/ZDY9+FTJGwbzfavU9EZEo1jGncahHlLeurSIpyVB0YE5IjxvPFJRF4kFalhOWP3gE6ir2ffzpG2DHR3DWHTBMIwkiItEsJS2ZlPTkkAfl0jVVjJ4wgpR0TXrWXwrKIvHi6MuhvRX+d2/X7R89Bf+7B6Z/Ayac4kppIiIyMJkhnku5sa6FytI6xqrtYkAUlEXixciJMOl0WH0v+BudbfWV8PjXYdQhMPNGd+sTEZF+c4Jy6EaUfeuqwULJ5JEhO2YiUFAWiSfHXgm7dzoLilgLy/4PmnbBOX+C5FS3qxMRkX7KzE6lPoTTw21dW0XqsGTy9xsesmMmAjWpiMSTcSfCiGL4z7Xw728626Z8AUYf4m5dIiIyIJm5aTTuaqHN347HO7RxTWstpWuqKD4ol6Qk3cw9EBpRFokn7z8EDdudXuUO65bBe0vcq0lERAYsI9v5FLChduijytXlu2moaVZ/8iAoKIvEk2dvgraWrtv8jc52ERGJGcNzQjeXculaZ9nq4smaFm6gFJRF4kmtb2DbRUQkKmUEVuerqxr6iPLWNVVkjx7GiJFatnqgFJRF4klW8cC2i4hIVOpYxrphiDf0tfnb2fZRtVbjGyQFZZF4MvNG8HYbMfCma2o4EZEYs2fRkaqhtV6Ub6yh1d/O2MkKyoOhoCwST6Z+Aeb/DrJKAOP8Of93znYREYkpmTlDnyKudG0VSR7DmAOzQ1NUgtH0cCLxZuoXFIxFROJAZk7akBcd2bqmioIJWaSkKfINhkaURURERKLQUJex3r2rhR2l9epPHgIFZREREZEolJmTSmOdnzZ/+6Ce71vnTAun+ZMHT0FZREREJAp1zHwx2D7l0jVVpGV4ySvRstWDpaAsIiIiEoUyh7DoiLWWrWurKJ6co2Wrh0BBWURERCQK7RlRHsQNfVXbGthd20KJpoUbEgVlERERkSiUkd0RlAc+otyxbLWC8tAoKIuIiIhEoZS0ZFKHJQ9qRLl0TRU5BcMYnpsWhsoSh4KyiIiISJRypogbWFBu9bdRtqFG08KFgIKyiIiISJTKyE4bcOtF+ce1tPnb1XYRAgrKIiIiIlEqMzeVhgFOD1e6xlm2uujAnDBVlTgUlEVERESiVGa2s+hIq7+t38/ZuraKwv2z8KZ6wlhZYlBQFhEREYlSHXMp93dUuaG2mZ2+erVdhIiCsoiIiEiUyswNTBFX1b+g7FtXDcDYg0eGraZEoqAsIiIiEqUyswe2jHXpmirSMr3kFWeGs6yEoaAsIiIiEqUGsox1x7LVJZNzMVq2OiQUlEVERESilDfV4yw60o/Wi51lDTTu0rLVoaSgLCIiIhLFMnPS+tV6UbpGy1aHmoKyiIiISBRzVufru/WidO1OcsdkkJmTGoGqEoOCsoiIiEgU688y1q0tbWzbUKvR5BBTUBYRERGJYpk5qTTV+2lt6XnRkW0f19DW2k7JwQrKoaSgLCIiIhLF9sx80UufcumaKpKSDWMOyI5QVYlBQVlEREQkimUEeo57a78oXVvFmP2z8aZo2epQUlAWERERiWLDO5ax7uGGvobaZnaWNag/OQwUlEVERESiWMeIcl0PI8qlawPTwqk/OeQUlEVERESimDfFQ2pGMg29BOX04V7yirRsdagpKIuIiIhEucyctKBzKdt2S+naai1bHSYKyiIiIiJRLjMnNWjrxc5t9c6y1Wq7CAsFZREREZEol5mTFrT1YquWrQ4rBWURERGRKJeZnUpTgx9/t0VHStdUMbIog4wsLVsdDgrKIiIiIlEuM9cJwp1Hlf0tbZR/rGWrw0lBWURERCTKZWZ3LDqy94a+8g1atjrcFJRFREREolywZay3rq3Ck5zEmP2zXaoq/ikoi4iIiES5PctYV+0NyqVrqhhzQBbJWrY6bBSURURERKKcN8VDWoZ3z4hyQ00zVdsaKJk80uXK4puCsoiIiEgMyMhJ3dOjrGWrIyOiQdkYc68xZpUx5oeRPK+IiIhIrBuek0p9YNaLrWuqSB+RwsiiDJerim8RC8rGmLMBj7X2OGCMMeaASJ1bREREJNZlBJaxtu0W37oqxk7OxRgtWx1OkRxRPgVYEvh+JXBCBM8tIiIiEtMyc1JpbmilYlMtjXV+tV1EQCSDcgZQFvh+FzC684PGmMuNMauNMasrKysjWJaIiIhI9BsemPli7apyAIoPynGznIQQyaBcD6QHvs/sfm5r7d3W2mnW2mn5+fkRLEtEREQk+mUE5lLe8OZ2RhZnatnqCIhkUH6Tve0WhwFbInhuERERkZhWuXUXAK3Nbeza0chHr1e4XFH8S47guZYCLxljxgCnA8dG8NwiIiIiMeuj1yt449+b9/zsb2rjuQfWAXDgMQVulRX3IjaibK3dhXND32vAqdba2kidW0RERCSWvfr4Rlr97V22tba08+rjG12qKDFEckQZa201e2e+EBEREZF+6Lx0dX+2S2hoZT4RERGRKJeZG/zGvZ62S2goKIuIiIhEuemfm0hyStfYlpySxPTPTXSposQQ0dYLERERERm4jhv2Xn18I/VVzWTmpjL9cxN1I1+YKSiLiIiIxIADjylQMI4wtV6IiIiIiAShoCwiIiIiEoSCsoiIiIhIEArKIiIiIiJBKCiLiIiIiAShoCwiIiIiEoSCsoiIiIhIEArKIiIiIiJBKCiLiIiIiAShoCwiIiIiEoSCsoiIiIhIEArKIiIiIiJBKCiLiIiIiAShoCwiIiIiEoSCsoiIiIhIEMZa63YN+zDGVAKfuHT6PGCHS+dOFLrGkaHrHH66xpGh6xwZus7hp2scGQO9zvtZa/ODPRCVQdlNxpjV1tppbtcRz3SNI0PXOfx0jSND1zkydJ3DT9c4MkJ5ndV6ISIiIiIShIKyiIiIiEgQCsr7utvtAhKArnFk6DqHn65xZOg6R4auc/jpGkdGyK6zepRFRERERILQiLKIiIiISBAKyiIiIiJhYIzJNcacZozJc7uWeBbO66yg3Ikx5l5jzCpjzA/driUeGWOSjTFbjTHPB76muF1TvDHGjDbGvBT43muM+U/gNX2J27XFi27XuMgY4+v0mg46D6f0nzEmyxjzhDHmaWPMY8aYFP3bHHo9XGf9+xxCxphCYDlwNPCcMSZfr+XQ6+E6h+y1rKAcYIw5G/BYa48DxhhjDnC7pjg0FXjQWntK4Ot9twuKJ8aYHOA+ICOw6ZvA6sBr+gxjzHDXiosTQa7xMcDPOr2mK92rLm5cCPzWWnsaUAGcj/5tDofu13kh+vc51A4BrrXW/gxYAcxAr+Vw6H6dLyGEr2UF5b1OAZYEvl8JnOBeKXHrWOAsY8zLxpgHjDHJbhcUZ9qA84BdgZ9PYe9rehWgSe6Hrvs1Phb4ujHmVWPMre6VFT+stX+01j4d+DEfuAj92xxyQa5zK/r3OaSstc9Ya18zxpyEM9o5B72WQy7IdW4khK9lBeW9MoCywPe7gNEu1hKv/gecbK09AagBPutuOfHFWrvLWlvbaZNe0yEW5Bo/ARxnrZ0OHGiMmepSaXHHGDMdyAFK0es4bDpd56fRv88hZ4wxOG+u/YBBr+Ww6Had3yWEr2UF5b3qgfTA95no2oTDe9ba8sD36wB97BReek2H3yprbV3ge72mQ8QYkwv8HucjVL2Ow6Tbdda/z2FgHVfhfKp3LHoth0W361wQytey/pL2epO9H4McBmxxr5S49XdjzGHGGA9wFs67PgkfvabDb4UxptAYMwznY9UP3C4o1hljUnA+nr7eWvsJeh2HRZDrrH+fQ8wY8z1jzJcCP2YDN6PXcsgFuc53hvK1rAVHAowxI4CXgGeB04Fju33EKkNkjDkU+AfOx0//ttb+wOWS4pIx5nlr7SnGmP2A/wLPAMfhvKbb3K0uPnS6xqcCdwAtwN3W2j+4XFrMM8ZcCfycvf9z+wvwLfRvc0gFuc7PAeegf59DJnDz7xIgFedN9PXAi+i1HFJBrvMdwAOE6LWsoNxJ4GKfBrxora1wux6RoTLGjMEZwVihf5AlVunfZokXei3HHgVlEREREZEg1KMsIiIiIhKEgrKIiIiISBCaUFxEJIYEJs/3AsOBkUAJMAm401rrD7J/irW2JbJViojEB40oi4jECGPMZ3HmCf0PzowmPwUOBrYDw4LsPymwb8fPGhwRERkA3cwnIhIDAitPJXVM8WeMOR9otdY+HHgsG6gD7gImAA2dn44zMNJsrV0QybpFRGKZRhdERGLDAcAfjDF+nCBcAlQZY34FrMFpx7gMaAOuAhqBRdbaLxtjZgGnAD9xo3ARkVilEWURkRgSaL+4FFgJVOHMyboauMda22KMuQcoxZk/exLwFpAX+NpgrZ3vSuEiIjFIPcoiIjHAGHOoMeYh4FTgfmA0ThB+FNgNPG2MOR1Iw2m/+CqwASdEv4cTpBWSRUQGQCPKIiIxIHAj3jAgAzgQJxDfhDOyfKO11h/oVX4cZ2nim4BWnOW1wWnF+H/W2tZI1y4iEqvUoywiEhtGAEuApsDP6UARMAV4zBiTClwPDLfWvmaMmQ8sA64EvgSsU0gWERkYBWURkRhgra0CZnX8bIwpBn5hrf1/nbYdDJQF9m82xtwC/A94BfhNZCsWEYl96lEWEYkxgZD8EE7/cWfjgTuNMXcYYx4EZgBnAE8D9xljXjHGzIxstSIisUs9yiIiMcgYY6z+ARcRCSsFZRERERGRINR6ISIiIiIShIKyiIiIiEgQCsoiIiIiIkEoKIuIiIiIBKGgLCIiIiISxP8HzgZUjzDJIZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 筛选2018年1月至2019年9月的数据  \n",
    "mask = (df['Accper'] >= '2018-01-01') & (df['Accper'] <= '2019-09-30')\n",
    "df_period = df.loc[mask]\n",
    "\n",
    "# 筛选出合并报表的数据并按照行业大类进行分组，然后计算每个季度的利润率均值  \n",
    "df_grouped = df_period[df_period['Typrep'] == 'A'].groupby(['Indnme', df_period['Accper'].dt.to_period('Q')])['利润率'].mean()\n",
    "\n",
    "\n",
    "df_plot = df_grouped.reset_index()\n",
    "\n",
    "# 使用matplotlib绘制折线图  \n",
    "plt.figure(figsize=(12,8))\n",
    "for industry in df_plot['Indnme'].unique():\n",
    "    data = df_plot[df_plot['Indnme'] == industry]['利润率']\n",
    "    plt.plot(data.index, data.values, label=industry, marker='o')\n",
    "\n",
    "plt.title('2018 年1月至2019 年9月各行业大类利润率变化')\n",
    "plt.xlabel('季度')\n",
    "plt.ylabel('利润率均值')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7780d880386e080d",
   "metadata": {},
   "source": [
    "# Task2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c8f7c3e6b08dba5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:42.604426200Z",
     "start_time": "2023-11-12T01:32:42.556424100Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年9月营业利润率均值排名第1的行业大类是： ����\r\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../result/LR_new.csv')\n",
    "# 将Accper列转换为日期格式  \n",
    "data['Accper'] = pd.to_datetime(data['Accper'])\n",
    "\n",
    "# 定义日期范围，筛选2018年9月份的数据  \n",
    "mask = (data['Accper'] > '2018-08-31') & (data['Accper'] <= '2018-09-30')\n",
    "df_sep = data.loc[mask]\n",
    "\n",
    "# 再筛选出合并报表的数据  \n",
    "data_filtered  = df_sep[df_sep['Typrep'] == 'A']\n",
    "\n",
    "# 计算每个行业大类的营业利润率均值  \n",
    "profit_margin_mean = data_filtered.groupby('Indnme')['利润率'].mean()\n",
    "\n",
    "# 找出营业利润率均值排名第1的行业大类  \n",
    "top_industry = profit_margin_mean.idxmax()\n",
    "\n",
    "print(\"2019年9月营业利润率均值排名第1的行业大类是：\", top_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed73085e7a41e695",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:43.103196500Z",
     "start_time": "2023-11-12T01:32:43.035195500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算目标行业大类的每个细类的营业利润率  \n",
    "data_for_plot = data_filtered[data_filtered['Indnme'] == top_industry]\n",
    "profit_margins = data_for_plot.groupby('Nindnme')['利润率'].mean()\n",
    "\n",
    "# 提取利润率排名前3的细类  \n",
    "top3_nindnme = profit_margins.nlargest(3).index\n",
    "\n",
    "# 绘制柱状图  \n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(top3_nindnme, profit_margins[top3_nindnme])\n",
    "ax.set_xlabel('细类')\n",
    "ax.set_ylabel('利润率')\n",
    "ax.set_title(f'2019年{top_industry}各细类利润率对比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1b72f33d95c362e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:32:50.692960600Z",
     "start_time": "2023-11-12T01:32:50.622893200Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 确定利润率排名第1的细类  \n",
    "profit_margins_by_nindnme = data_filtered[data_filtered['Indnme'] == top_industry].groupby('Nindnme')['利润率'].mean()\n",
    "top_nindnme = profit_margins_by_nindnme.idxmax()\n",
    "\n",
    "# 在该细类中找出利润率排名前5的企业  \n",
    "data_for_plot = data_filtered[(data_filtered['Indnme'] == top_industry) & (data_filtered['Nindnme'] == top_nindnme)]\n",
    "profit_margins_by_stkcd = data_for_plot.groupby('Stkcd')['利润率'].mean()\n",
    "top5_stkcd = profit_margins_by_stkcd.nlargest(5).index\n",
    "\n",
    "# 绘制柱状图  \n",
    "fig, ax = plt.subplots()\n",
    "ax.bar( profit_margins_by_stkcd[top5_stkcd],profit_margins_by_stkcd[top5_stkcd])\n",
    "ax.set_xlabel('企业')\n",
    "ax.set_ylabel('利润率')\n",
    "ax.set_title(f'2019年{top_industry}中{top_nindnme}的企业利润率对比')\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc5bff8b0529b59d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:35:26.846314800Z",
     "start_time": "2023-11-12T01:35:26.837297Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stkcd', 'Accper', 'Typrep', 'B001100000', 'B001101000', 'B001200000',\n",
       "       'B001201000', 'B001207000', 'B001209000', 'B001210000', 'B001211000',\n",
       "       'B001212000', 'B001301000', 'B001302000', 'B001302101', 'B001304000',\n",
       "       'B001300000', 'B001400000', 'B001500000', 'B001000000', 'B002100000',\n",
       "       'B002000000', 'B002000101', 'B002000201', 'B003000000', 'B004000000',\n",
       "       'B005000000', 'B006000000', 'B006000101', 'B006000102', 'A002000000',\n",
       "       'A001000000', 'Indnme', 'Nindnme', '利润率', '资产负债率'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dfc14cd876d94d50",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T01:44:32.842055400Z",
     "start_time": "2023-11-12T01:44:32.756051600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 筛选出企业\"T1\"在2019年9月的数据  \n",
    "t1_stkcd = top_nindnme  # 企业\"T1\"的证券代码  \n",
    "data_for_t1 = data_filtered[(data_filtered['Indnme'] == top_industry) & (data_filtered['Nindnme'] == t1_stkcd)]\n",
    "\n",
    "# 提取相关数据  \n",
    "operating_cost = data_for_t1['B001201000'].values[0]  # 营业成本  \n",
    "tax = data_for_t1['B001207000'].values[0]  # 营业税金及附加  \n",
    "selling_expense = data_for_t1['B001209000'].values[0]  # 销售费用  \n",
    "management_expense = data_for_t1['B001210000'].values[0]  # 管理费用  \n",
    "financial_expense = data_for_t1['B001211000'].values[0]  # 财务费用  \n",
    "\n",
    "# 创建饼图数据  \n",
    "labels = ['营业成本', '营业税金及附加', '销售费用', '管理费用', '财务费用']\n",
    "sizes = [operating_cost, tax, selling_expense, management_expense, financial_expense]\n",
    "explode = (0.1, 0, 0, 0, 0)  # 将第一个部分（营业成本）稍微移出中心以强调  \n",
    "\n",
    "# 绘制饼图  \n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "ax.axis('equal')  # 确保饼图是圆形的  \n",
    "plt.title(f'2019年{top_industry}企业“T1”营业总成本分析')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "611962f3b12294de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-12T02:02:56.110879Z",
     "start_time": "2023-11-12T02:02:55.926898700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask =  (data['Indnme'] == top_industry) & (data['Nindnme'] == t1_stkcd)\n",
    "df_t1 = data.loc[mask]\n",
    "\n",
    "mask1 = ((data['Accper'] > '2019-08-31') & (data['Accper'] <= '2019-09-30'))| ((data['Accper'] >= '2019-03-01') & (data['Accper'] <= '2019-03-31')) | ((data['Accper'] >= '2019-06-01') & (data['Accper'] <= '2019-06-30'))\n",
    "\n",
    "df_t1 = df_t1[mask1]\n",
    "\n",
    "\n",
    "# 绘制图表  \n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# 绘制柱状图 - 营业总收入、营业总成本  \n",
    "ax1.bar(df_t1['Accper'], df_t1['B001100000'], label='营业总收入')\n",
    "ax1.bar(df_t1['Accper'], df_t1['B001200000'], label='营业总成本')\n",
    "ax1.set_xlabel('会计期间')\n",
    "ax1.set_ylabel('金额')\n",
    "ax1.legend()\n",
    "\n",
    "# 绘制折线图 - 利润率、资产负债率，共享x轴  \n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df_t1['Accper'], df_t1['利润率'], color='g', label='利润率')\n",
    "ax2.plot(df_t1['Accper'], df_t1['资产负债率'], color='r', label='资产负债率')\n",
    "ax2.set_ylabel('比率')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "# 设置标题  \n",
    "plt.title('2019年月企业“T1”经营情况分析')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da5cb7b04b38e93d",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
